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Google Introduces New Class of Cheap AI Models as Cost Concerns Intensify

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Google has introduced new, cost-effective AI models under its Gemini family, responding to increasing competition and concerns over the escalating costs of artificial intelligence. The new offerings, including the “Flash-Lite” model, are designed to compete with cheaper AI models like DeepSeek’s, a Chinese rival that has drawn attention for its low-cost AI training.

The company unveiled several versions of its Gemini 2.0 models, which offer varying levels of performance and pricing. Among these is the “Gemini 2.0 Flash,” which was released to the general public after being previewed to developers in December. Flash-Lite, a more affordable variant, has been developed in response to positive feedback on the earlier Flash 1.5 model. However, the cost of Gemini 2.0 Flash is higher than its predecessor.

Google’s new pricing strategy comes amid growing scrutiny from investors over the rising expenses of AI model development. Recently, DeepSeek revealed it spent just $6 million on the final training run of one of its models, prompting comparisons to the significantly higher costs cited by major U.S. AI firms, including Alphabet, Microsoft, and Meta. Despite this, DeepSeek’s low-cost model has spurred competitors to accelerate their AI spending, leading to concerns about the long-term profitability of such investments.

Pricing for Gemini Flash-Lite is competitive, with certain inputs costing as little as $0.019 per 1 million tokens. This is cheaper than OpenAI’s flagship model, which costs $0.075 per million tokens, and slightly higher than DeepSeek’s $0.014 model (though DeepSeek’s pricing will rise fivefold on February 8).

These updates reflect Alphabet’s response to the growing pressure to provide affordable AI models while maintaining a competitive edge in the rapidly evolving AI space. However, despite these advancements, investor concerns remain about the sustainability of high capital expenditures in AI development.

 

Alphabet Shares Drop Amid Cloud Growth Concerns and Rising AI Spending

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Alphabet’s stock dropped by 8% on Wednesday, driven by investor concerns over the company’s slowing cloud growth and planned capital expenditures of $75 billion for the year. This marks a significant shift for the Google parent, highlighting fears surrounding the escalating costs of artificial intelligence (AI) development.

The company’s quarterly cloud revenue grew by 30%, slower than the 35% increase seen in the previous quarter, and missed market expectations. This decline mirrors challenges faced by its larger cloud rival, Microsoft. Analysts have indicated that these results mark a shift in Google’s business model, moving from its capital-light, high-margin search advertising business to a more capital-intensive, AI-driven approach.

The projected increase in capital expenditures (CapEx) for 2025 is 29% higher than analysts’ estimates. Alphabet has indicated that it will prioritize costly AI investments to avoid falling behind competitors, a strategy that has raised concerns among investors looking for a clearer path to AI-driven profits. Analysts such as Gil Luria from D.A. Davidson expressed worry that Alphabet might be heading down the same path as Microsoft, facing the challenges of high AI costs without immediate returns.

Alphabet’s concerns were further compounded by the rise of China’s DeepSeek, a low-cost AI model that has spurred debate about the high expenses of AI development by Big Tech companies. Despite better-than-expected ad revenue performance, the heightened CapEx and cloud struggles have overshadowed the positive results.

Analysts have responded to the concerns by cutting their price targets on Alphabet’s stock, with some expressing doubts about the company’s ability to capture a significant share of the cloud market. Alphabet’s shares remain the cheapest among the major U.S. cloud providers, with a 12-month forward price-to-earnings ratio of 22.7, lower than Amazon’s and Microsoft’s ratios.

 

European Data Centre Space Shortage Expected in 2025 as AI Booms

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As artificial intelligence (AI) continues to surge, Europe’s data centres are facing a growing capacity crisis. Despite plans to expand by 22% in 2025, experts warn that demand will outpace supply, risking Europe’s further delay in the AI race. Analysts at the Kickstart Europe conference on Wednesday highlighted the growing concerns about electric grid congestion and a lack of suitable sites for new data centres, particularly in traditional European hubs like Frankfurt, London, Amsterdam, Paris, and Dublin.

One of the key developments exacerbating this issue is the rise of China’s DeepSeek, which has introduced more energy-efficient AI models. While this development may ease some of the pressure, it does not address Europe’s significant infrastructure constraints.

Major software companies like Google and Amazon are continuing to push ahead with plans for hyperscale data centres, but they, along with European firms, are struggling to find adequate space. “Providers can’t build supply fast enough to keep up with demand,” said Kevin Restivo, director of data centre research at CBRE, during his keynote address.

The shortage is most pronounced in primary data centre locations, but secondary markets like Milan, Warsaw, and Berlin are seeing rapid growth in 2025. Many companies are even looking outside of urban areas to find space. CBRE forecasts that 9.1 gigawatts of new capacity will come online this year, with hyperscalers taking up over a third of it.

However, Europe’s data centre expansion still lags behind U.S. investments, which are seeing massive funding, such as the $500 billion “Stargate” initiative involving Oracle, Microsoft, and OpenAI over the next four years. According to CBRE, the average cost to build data centre space in Europe is 12 million euros per megawatt, suggesting that the European market is expanding by over 100 billion euros this year. Despite this growth, analysts, such as Stijn Grove from the Dutch Data Center Association, warn that Europe risks falling behind in the AI race, becoming technologically dependent on the U.S. and China.